Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit

Bollas, GM; Papadokonstadakis, S; Michalopoulos, J; Arampatzis, G; Lappas, AA; Vasalos, IA; Lygeros, A

dc.contributor.author	Bollas, GM	en
dc.contributor.author	Papadokonstadakis, S	en
dc.contributor.author	Michalopoulos, J	en
dc.contributor.author	Arampatzis, G	en
dc.contributor.author	Lappas, AA	en
dc.contributor.author	Vasalos, IA	en
dc.contributor.author	Lygeros, A	en
dc.date.accessioned	2014-03-01T01:19:41Z
dc.date.available	2014-03-01T01:19:41Z
dc.date.issued	2003	en
dc.identifier.issn	0255-2701	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15658
dc.subject	FCC kinetics	en
dc.subject	Fluid catalytic cracking	en
dc.subject	Hybrid modeling	en
dc.subject	Neural networks	en
dc.subject	Pilot to commercial unit scale up	en
dc.subject	Process modeling	en
dc.subject	Riser hydrodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Petroleum refineries	en
dc.subject.other	Pilot plants	en
dc.subject.other	Oil refinery	en
dc.subject.other	Fluid catalytic cracking	en
dc.subject.other	coke	en
dc.subject.other	catalytic cracking	en
dc.subject.other	hydrodynamics	en
dc.subject.other	industrial plant	en
dc.subject.other	kinetics	en
dc.subject.other	neural network	en
dc.subject.other	pilot plant	en
dc.subject.other	artificial neural network	en
dc.subject.other	fluid catalytic cracking	en
dc.subject.other	Greece	en
dc.subject.other	model	en
dc.subject.other	oil industry	en
dc.subject.other	petrochemical industry	en
dc.subject.other	scale up	en
dc.title	Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0255-2701(02)00206-4	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0255-2701(02)00206-4	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze. (C) 2003 Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Chemical Engineering and Processing: Process Intensification	en
dc.identifier.doi	10.1016/S0255-2701(02)00206-4	en
dc.identifier.isi	ISI:000183738500010	en
dc.identifier.volume	42	en
dc.identifier.issue	8-9	en
dc.identifier.spage	697	en
dc.identifier.epage	713	en